Chronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG)
- 1st Edition - January 16, 2022
- Imprint: Academic Press
- Authors: Archana Bajirao Kanwade, Vinayak Bairagi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 0 5 0 - 8
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 8 8 6 0 8 - 6
Chronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG) presents a new and innovative method of COPD diagnosis using EMG to analyze sternomas… Read more
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Request a sales quoteChronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG) presents a new and innovative method of COPD diagnosis using EMG to analyze sternomastoid muscle activity using features extraction and classification. The book describes the methodology of EMG analysis, the slope-based onset detection algorithm and SEMG analysis in time, frequency and time frequency domain analyses. It also explores the identification of frequencies for single frequency Continuous Wavelet Transform (CWT) analysis and feature extraction and selection for successful classification COPD into its severity grades.
The book provides a compilation of all techniques used in the literatures and emphasizes newly proposed techniques for the early detection of COPD. Fully comprehensive, the book includes discussion of limitations of existing methods for COPD diagnosis and introduces new efficient methods for COPD identification, classification and early diagnosis.
- Provides an easy, simple and comprehensive guide to using EMG analysis for COPD diagnosis
- Presents detailed explanations of the recently developed slope-based onset detection algorithm for muscle activity detection, along with numerous original figures, tables and graphs to aid interpretation
- Includes a complete review of various features, such as extraction using single frequency CWT analysis and the feature selection algorithm for COPD diagnosis
Researchers, academics and scientists working in the field of Respiratory diseases, Biomedical Engineering, EMG signal processing and Pulmonologists. Biomedical Signal Processing Course specifically for the EMG signal Processing details. Respiratory medicine branch for Chronic Obstructive Pulmonary Disease subject
1 INTRODUCTION
1.1 Chronic Obstructive Pulmonary Disease
1.2 Respiratory Mechanics
1.3 Electromyography
1.4 Motivation
1.5 Need of Research
2 METHODOLOGY
2.1 Introduction
2.2 Methodology
2.2.1 Sample Selection, Data Collection, and Experimental Setup
2.3 Recording Techniques
2.4 Skin Preparations
2.5 EMG Affecting Factors
2.6 EMG Noise
2.7 EMG analysis
2.7.1 Time domain analysis techniques
2.7.2 Frequency domain analysis techniques
2.7.3 Time-Frequency domain analysis techniques
2.8 Feature Selection and Classification
2.9 Summary
3 COPD AND HEALTHY CLASSIFICATION
3.1 Introduction
3.2 Methodology
3.3 Time Domain analysis
3.4 Onset Detection Algorithm
3.4.1 Improved Slope Based Onset Detection Algorithm
3.5 Results of Classification
3.5.1 Performance Evaluation
3.5.2 Feature Selection
3.5.3 Classification
3.6 Summary
4 COPD GRADE CLASSIFICATION
4.1 Introduction
4.2 Frequency Domain Analysis
4.2.1 Power Spectral Density analysis
4.2.2 Spectrum at window length of 15 samples
4.2.3 Spectrum analysis at onset and offset area
4.3 Time-Frequency Domain Analysis
4.3.1 Low-Frequency Region
4.3.2 High-Frequency Region
4.4 Feature Selection Algorithm
4.4.1 Presented Feature Selection Algorithm
4.4.2 Backward Elimination using Regression
4.4.3 Analysis of features using Weka tool
4.5 Results
5 EARLY DETECTION OF COPD
5.1 Introduction
5.2 Early Diagnosis Model
5.3 Results and Summary
6 RESULTS AND DISCUSSION
6.1 Introduction
6.2 Slope Based Onset Detection Algorithm
6.3 COPD and Healthy Classification
6.4 COPD Grade Classification
6.5 Summary
7 CONCLUSION
7.1 Conclusion
7.2 Research Contributions
7.3 Future Scope
Bibliography
- Edition: 1
- Published: January 16, 2022
- No. of pages (Paperback): 192
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780323900508
- eBook ISBN: 9780323886086
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Archana Bajirao Kanwade
Dr. Archana Bajirao Kanwade has completed M.E. in Electronic-Communication in 2009 from Shivaji University. University of Pune has awarded her a PhD degree in Electronics and Telecommunication Engineering in 2020. She has 17 years of teaching experience and 09 years of research experience. She has filed a patent on COPD diagnosis using EMG analysis. She has published more than 35 technical papers, out of which 28 papers are in International journals. She has 4 papers indexed in Scopus Journals and 2 papers in SCI indexed journals. She is reviewer for three Scientific Journals.
She has also been invited as resources person for invited talk. She is a member of IEEE SIGHT. Currently she is associated with Sinhgad Institute of Technology and Science, (Affiliated college to S P Pune University), India as Assistant Professor in Electronics and Telecommunication Engineering. She is recognized PG Guide in Electronics and Telecommunication Engineering of Savitribai Phule Pune University.
Affiliations and expertise
Assistant Professor, Department of E&TC, Sinhgad Institute of Technology and Science, Narhe , SPPU, Pune, IndiaVB
Vinayak Bairagi
Dr. Vinayak K. Bairagi, is a recognized PhD guide in Savitribai Phule Pune University. He is working as Professor at Department of E electronics and Telecommunication Engg. and actively working as Chairman, IEEE Signal Processing Society Pune Chapter. He has teaching experience of 14 years and research experience of 10 years. He has filed 12 patents and 5 copyrights in technical field. He has published more than 70 papers. He has received IEI national level Young Engineer Award (2014) and ISTE national level Young Researcher Award (2015) for his excellence in the field of engineering. He also has 5 books and 6 book chapters on his credits. His area of interest is Biomedical Signal Processing and Brain Imaging.
Affiliations and expertise
PhD Mentor in Electronics Engineering, Savitribai Phule Pune University, Pune, Maharashtra, IndiaRead Chronic Obstructive Pulmonary Disease (COPD) Diagnosis using Electromyography (EMG) on ScienceDirect